import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
__a    :	Optional[Any]         =    {
    """/attention/""": """/0/SelfAttention/""",
    """/self_attention/""": """/0/SelfAttention/""",
    """/encoder_decoder_attention/""": """/1/EncDecAttention/""",
    """value""": """v""",
    """query""": """q""",
    """key""": """k""",
    """out""": """o""",
    """pre_self_attention_layer_norm""": """0/layer_norm""",
    """pre_cross_attention_layer_norm""": """1/layer_norm""",
    """pre_attention_layer_norm""": """0/layer_norm""",  # previously 1, but seems wrong
    """token_embedder""": """shared""",
    """encoder_norm""": """final_layer_norm""",
    """decoder_norm""": """final_layer_norm""",
    """relpos_bias/rel_embedding""": """block/0/layer/0/SelfAttention/relative_attention_bias/weight""",
    """router/router_weights/w/""": """router/classifier/""",
    """roer/roer_weights/w/""": """router/classifier/""",
    """logits_dense""": """lm_head""",
}





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				__lowercase					     =				list(s_dict.keys()				)
				for key in keys:
								__lowercase					     =				r'''.*/layers_(\d+)'''
								__lowercase					     =				key
								if re.match(lowercase				,     lowercase				):
												__lowercase					     =				re.sub(r'''layers_(\d+)'''				,     r'''block/\1/layer'''				,     lowercase				)

								__lowercase					     =				r'''(encoder|decoder)\/'''

								if re.match(lowercase				,     lowercase				):
												__lowercase					     =				re.match(lowercase				,     lowercase				).groups()
												if groups[0] == "encoder":
																__lowercase					     =				re.sub(r'''/mlp/'''				,     r'''/1/mlp/'''				,     lowercase				)
																__lowercase					     =				re.sub(r'''/pre_mlp_layer_norm/'''				,     r'''/1/layer_norm/'''				,     lowercase				)

												elif groups[0] == "decoder":
																__lowercase					     =				re.sub(r'''/mlp/'''				,     r'''/2/mlp/'''				,     lowercase				)
																__lowercase					     =				re.sub(r'''/pre_mlp_layer_norm/'''				,     r'''/2/layer_norm/'''				,     lowercase				)

        # 2. Convert other classic mappings
								for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
												if old_key in new_key:
																__lowercase					     =				new_key.replace(lowercase				,     lowercase				)

								print(F"{key} -> {new_key}"				)
								__lowercase					     =				s_dict.pop(lowercase				)

				if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
								__lowercase					     =				s_dict[
								    '''encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
								].T
				if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
								__lowercase					     =				s_dict[
								    '''decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
								].T

				# 3. Take extra care of the EXPERTS layer
				for key in list(s_dict.keys()				):
								if "expert" in key:
												__lowercase					     =				s_dict[key].shape[0]
												__lowercase					     =				s_dict[key]
												for idx in range(lowercase				):
																__lowercase					     =				expert_weihts[idx]
																print(F"{key} -> {key.replace('expert/'				,     'nested fstring'				)}"				)

												s_dict.pop(lowercase				)

				return s_dict


__a    :	Dict         =    {
    """NUM_ENCODER_LAYERS""": """num_layers""",
    """NUM_DECODER_LAYERS""": """num_decoder_layers""",
    """NUM_HEADS""": """num_heads""",
    """HEAD_DIM""": """d_kv""",
    """EMBED_DIM""": """d_model""",
    """MLP_DIM""": """d_ff""",
    """NUM_SELECTED_EXPERTS""": """num_selected_experts""",
    """NUM_ENCODER_SPARSE_LAYERS""": """num_sparse_encoder_layers""",
    """NUM_DECODER_SPARSE_LAYERS""": """num_sparse_decoder_layers""",
    """dense.MlpBlock.activations""": """feed_forward_proj""",
}





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				import regex as re

				with open(lowercase				,     '''r'''				) as f:
								__lowercase					     =				f.read()

				__lowercase					     =				re.findall(r'''(.*) = ([0-9.]*)'''				,     lowercase				)
				__lowercase					     =				{}
				for param, value in regex_match:
								if param in GIN_TO_CONFIG_MAPPING and value != "":
												__lowercase					     =				float(lowercase				) if '''.''' in value else int(lowercase				)

				__lowercase					     =				re.findall(r'''(.*activations) = \(\'(.*)\',\)'''				,     lowercase				)[0]
				__lowercase					     =				str(activation[1]				)

				__lowercase					     =				num_experts
				__lowercase					     =				SwitchTransformersConfig(**lowercase				)
				return config





def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase=None				,     lowercase="./"				,     lowercase=8				):







				"""simple docstring"""



				print(F"Loading flax weights from : {flax_checkpoint_path}"				)
				__lowercase					     =				checkpoints.load_tax_checkpoint(lowercase				)

				if gin_file is not None:
								__lowercase					     =				convert_gin_to_config(lowercase				,     lowercase				)
				else:
								__lowercase					     =				SwitchTransformersConfig.from_pretrained(lowercase				)

				__lowercase					     =				SwitchTransformersForConditionalGeneration(lowercase				)

				__lowercase					     =				flax_params['''target''']
				__lowercase					     =				flatten_dict(lowercase				,     sep='''/'''				)
				__lowercase					     =				rename_keys(lowercase				)
				__lowercase					     =				unflatten_dict(lowercase				,     sep='''/'''				)

				# Load the flax params in the PT model
				load_flax_weights_in_pytorch_model(lowercase				,     lowercase				)

				print(F"Save PyTorch model to {pytorch_dump_path}"				)
				pt_model.save_pretrained(lowercase				)


if __name__ == "__main__":
		__a    :	List[str]         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--switch_t5x_checkpoint_path""",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        """The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"""
		        """ model architecture. If not provided, a `gin_file` has to be provided."""
		    ),
		)
		parser.add_argument(
		    """--gin_file""",
		    default=None,
		    type=str,
		    required=False,
		    help="""Path to the gin config file. If not provided, a `config_file` has to be passed   """,
		)
		parser.add_argument(
		    """--config_name""", default=None, type=str, required=False, help="""Config name of SwitchTransformers model."""
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output pytorch model."""
		)
		parser.add_argument("""--num_experts""", default=8, type=int, required=False, help="""Number of experts""")
		__a    :	Union[str, Any]         =    parser.parse_args()
		convert_flax_checkpoint_to_pytorch(
		    args.switch_tax_checkpoint_path,
		    args.config_name,
		    args.gin_file,
		    args.pytorch_dump_folder_path,
		    args.num_experts,
		)
import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
		print("""Googling.....""")
		__a    :	Tuple         =    """https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
		__a    :	List[str]         =    requests.get(url, headers={"""UserAgent""": UserAgent().random})
		# res.raise_for_status()
		with open("""project1a.html""", """wb""") as out_file:  # only for knowing the class
				for data in res.iter_content(1_0_0_0_0):
						out_file.write(data)
		__a    :	Dict         =    BeautifulSoup(res.text, """html.parser""")
		__a    :	Any         =    list(soup.select(""".eZt8xd"""))[:5]

		print(len(links))
		for link in links:
				if link.text == "Maps":
						webbrowser.open(link.get("""href"""))
				else:
						webbrowser.open(F'''https://google.com{link.get("href")}''')